Long-term improvements in sleep, pain, depression, and fatigue in older adults with comorbid osteoarthritis pain and insomnia
Bibliographic record
Abstract
In a primary care population of 327 older adults (age 60+) with chronic osteoarthritis (OA) pain and insomnia, we examined the relationship between short-term improvement in sleep or pain and long-term sleep, pain, depression, and fatigue by secondary analyses of randomized controlled trial data. Study participants, regardless of trial arm, were classified as Sleep or Pain Improvers with ≥30% baseline to 2-month reduction on the Insomnia Severity Index or the Brief Pain Inventory, respectively, or Sleep or Pain Non-Improvers. After controlling for trial arm and potential confounders, both Sleep and Pain Improvers showed significant (p < .01) sustained improvements across 12 months compared to respective Non-Improvers for the Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index, Brief Pain Inventory-short form (total, Interference, and Severity subscales), Patient Health Questionnaire, and Flinders Fatigue Scale. The effect sizes (Cohen's f2) for the sustained benefits in both Sleep and Pain Improvers compared to their respective Non-Improvers for all variables were small (<0.15) with the exception of medium effect size for sustained reduction in insomnia symptoms for the Sleep Improvers. We conclude that short-term sleep improvements in pain populations with comorbid insomnia precede benefits not only for long-term improvement in sleep but also for reduced pain over the long-term, along with associated improvements in depression and fatigue. Short-term improvements in pain appear to have similar long-term sequelae. Successfully improving sleep in pain populations with comorbid insomnia may have the additional benefits of improving both short- and long-term pain, depression, and fatigue. Trial Registration: OsteoArthritis and Therapy for Sleep (OATS) NCT02946957: https://clinicaltrials.gov/ct2/show/NCT02946957.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".